Chrome Data Analysis

Overview

This project aims to analyze and visualize Chrome browser data, including browsing history, bookmarks, extensions, and more. With scripts to process and analyze various types of data exported from Chrome, this project provides insights into browsing habits, bookmark trends, and other personal browsing data analytics.

Project Structure

The project is organized into two main directories: data and src.

.
├── README.md
├── data
│ ├── Chrome
│ │ ├── Addresses and more.json
│ │ ├── Bookmarks.html
│ │ ├── Device Information.json
│ │ ├── Dictionary.csv
│ │ ├── Extensions.json
│ │ ├── History.json
│ │ ├── OS Settings.json
│ │ ├── Reading List.html
│ │ └── Settings.json
│ └── tlds.csv
└── src
├── browsing_history.ipynb
└── tlds_extractor.ipynb

Data

• Chrome/: Contains exported Chrome data in various formats (JSON, HTML, CSV).
  • Addresses and more.json: Information on saved addresses and other data.
  • Bookmarks.html: Exported bookmarks.
  • Device Information.json: Information about connected devices.
  • Dictionary.csv: Custom dictionary words.
  • Extensions.json: Installed Chrome extensions.
  • History.json: Browsing history.
  • OS Settings.json: Chrome OS settings.
  • Reading List.html: Saved reading list items.
  • Settings.json: Chrome browser settings.
• tlds.csv: List of top-level domains used for data processing.

How to Download the Google Chrome Data

• Download your data from Google Takeout.
• Extract the downloaded zip file and navigate to the Chrome directory to find the exported data files.

Source Code

• src/: Contains Jupyter notebooks for data processing and analysis.
  • browsing_history.ipynb: Analyzes and visualizes browsing history data.
  • tlds_extractor.ipynb: Processes and extracts information from tlds.csv.

Getting Started

To run the analysis scripts, ensure you have Jupyter Notebook or JupyterLab installed. You may need to install additional Python packages such as pandas, numpy, and matplotlib for data processing and visualization. You can install these packages using pip:

pip install jupyterlab pandas numpy matplotlib

Then, navigate to the src directory and launch Jupyter:

cd src
jupyter notebook

Open the .ipynb files to view the analyses and visualizations.

Contributing

Contributions to the project are welcome! Whether it's adding new analyses, improving the existing notebooks, or fixing bugs, your help is appreciated. Please feel free to fork the repository and submit pull requests with your changes.

License

This project is open source and available under the MIT License.

Contact

For questions or suggestions, please open an issue in the GitHub repository.